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Cycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.

A stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.

First, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.

A novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.

Kelp is widely productive and inexpensive. The purpose of this study is to explore kelp liquid (KL) as an environment-friendly water-based lubricant, which is expected to replace some industrial lubricants and protect the environment while satisfying lubricating performance.

In this experiment, the soaked kelp was broken up by a wall-breaking machine to get the KL by a centrifuge. Elements and crystal structure of KL samples were characterized by X-ray photoelectron spectroscopy, X-ray diffraction and Raman spectra. The friction test is carried out by the relative movement of the polyethylene ball and the aluminum disk on the friction tester.

Friction experiments showed that 0.1 Wt.% KL has a good lubrication effect, and the average coefficient of friction is 0.063 under the condition of applying a 10 N load and moving at a speed of 2.0 cm/s. KL has good thermal conductivity with excellent cooling effect and high intermolecular force which makes high viscosity for excellent lubricating behavior, at the meantime molecules in solution remain stable which shows an excellent dispersibility.

At present, the research on kelp mainly focuses on its medicinal value and abundant nutritional value, and the research on its lubrication effect is less. Based on this situation, this paper explored the characteristics of KL as an environmentally friendly lubricant, which is expected to be used as a green cutting fluid.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.